Why does off-policy learning outperform on-policy learning?
This post contains many answers that describe the difference between on-policy vs. off-policy.
Your book may be referring to how the current (DQN-based) state-of-the-art (SOTA) algorithms, such as Ape-X, R2D2, Agent57 are technically &quot;off-policy&quot;, since they use a (very large!) replay buffer, often filled in a distributed manner. This has a number of benefits, such as reusing experience and not forgetting important experiences.
Another benefit is that you can collect a lot of experience distributedly. Since RL is typically not bottlenecked by the computation for training but rather from collecting experiences, the distributed replay buffer in Ape-X can enable much faster training, in terms of seconds but not sample complexity.
However, it's important to emphasize that these replay-buffer approaches are almost on-policy, in the sense that the replay buffer is constantly updated with new experiences. So, the policy in the replay buffer is &quot;not too different&quot; from your current policy (just a few gradient steps away). Most importantly, this allows the policy to learn from its own mistakes if it makes any...
Off-policy learning, in general, can also refer to batch RL (a.k.a. offline RL), where you're provided a dataset of experiences from another behavior policy, and your goal is to improve over it. Notably, you don't get to rollout your current policy in any way! In this case, algorithms that worked well with a replay-buffer (like DQN, SAC) fail miserably, since they over-estimate the value of actions when they extrapolate outside the &quot;support&quot; of the dataset. See the BCQ paper which illustrates how a lot of &quot;off-policy&quot; algorithms like DQN fail when the &quot;distance between the two policies is large&quot;. For this task, SOTA is a form of weighted behavioral cloning called Critic Regularized Regression (CRR).
It's also worth noting that importance sampling can correct off-policy gradients to be on-policy; but the farther away your target policy is, the larger the variance. This is especially deadly for long horizon tasks (often called curse of horizon).
To summarize, using replay-buffer (which makes the algorithm off-policy), especially a distributed one, can offer a lot of benefits over pure on-policy algorithms. However, this is a very special class of off-policy algorithms, where the behavioral policy is close to your policy.
But in general, off-policy is a lot harder than on-policy; you'll suffer from extrapolation bias if you use DQN-based approaches, and exponential variance blow-up if you use importance sampling to correct for it.